08-06-2013, 12:17 PM
Power Kites for Wind Energy Generation
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INTRODUCTION
The problems posed by electric energy generation from
fossil sources include high costs due to large demand
and limited resources, pollution and CO2 production,
and the geopolitics of producer countries. These problems
can be overcome by alternative sources that are renewable,
cheap, easily available, and sustainable. However, current
renewable technologies have limitations. Indeed, even the
most optimistic forecast on the diffusion of wind, photovoltaic,
and biomass sources estimates no more than a 20%
contribution to total energy production within the next
15–20 years.
THE KITEGEN PROJECT
To overcome the limitations of current wind power technology,
the KiteGen project was initiated at Politecnico di
Torino to design and build a new class of wind energy
generators in collaboration with Sequoia Automation,
Modelway, and Centro Studi Industriali. The project focus
[2], [3] is to capture wind energy by means of controlled
tethered airfoils, that is, kites; see Figure 1.
The KiteGen project has designed and simulated a
small-scale prototype (see Figure 2). The two kite lines are
rolled around two drums and linked to two electric drives,
which are fixed to the ground. The flight of the kite is controlled
by regulating the pulling force on each line. Energy
is collected when the wind force on the kite unrolls the
lines, and the electric drives act as generators due to the
rotation of the drums.
SYSTEM AND CONTROL TECHNOLOGIES
NEEDED FOR KITEGEN
Control Design
The main objective of KiteGen control is to maximize energy
generation while preventing the airfoils from falling to the
ground or the lines from tangling. The control problem can
be expressed in terms of maximizing a cost function that predicts
the net energy generation while satisfying constraints
on the input and state variables. Nonlinear model predictive
control (MPC) [11] is employed to accomplish these objectives,
since it aims to optimize a given cost function and fulfill
constraints at the same time. However, fast implementation is
needed to allow real-time control at the required sampling
time, which is on the order of 0.1 s. In particular, the implementation
of fast model predictive control (FMPC) based on
set membership approximation methodologies as in [12] and
[13] is adopted, see “How Does FMPC Work ?” for details.